{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cifar10数据集：\n",
    "    提供 5万张 32*32 像素点的十分类彩色图片和标签，用于训练。\n",
    "    提供 1万张 32*32 像素点的十分类彩色图片和标签，用于测试。\n",
    "    \n",
    "    code:\n",
    "    导入cifar10数据集：\n",
    "    cifar10 = tf.keras.datasets.cifar10\n",
    "    (x_train, y_train),(x_test, y_test) = cifar10.load_data()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train[0]:\n",
      " [[[ 59  62  63]\n",
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      "  [148 124 103]]\n",
      "\n",
      " [[ 16  20  20]\n",
      "  [  0   0   0]\n",
      "  [ 18   8   0]\n",
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      "\n",
      " [[ 25  24  21]\n",
      "  [ 16   7   0]\n",
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      "  [109  73  42]]\n",
      "\n",
      " [[ 33  25  17]\n",
      "  [ 38  20   4]\n",
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      "  [ 94  62  35]]\n",
      "\n",
      " [[ 50  32  21]\n",
      "  [ 59  32  11]\n",
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      "\n",
      " [[ 71  48  29]\n",
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      "\n",
      " [[ 97  69  40]\n",
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      "\n",
      " [[115  82  49]\n",
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      "\n",
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      "\n",
      " [[154 120  89]\n",
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      "\n",
      " [[154 122  94]\n",
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      "\n",
      " [[145 114  89]\n",
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      "\n",
      " [[142 115  86]\n",
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      "\n",
      " [[158 131  98]\n",
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      "\n",
      " [[145 115  79]\n",
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      "\n",
      " [[148 116  79]\n",
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      "\n",
      " [[149 115  79]\n",
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      "\n",
      " [[147 111  76]\n",
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      "\n",
      " [[152 114  80]\n",
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      "\n",
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      "  [ 83  53  34]]\n",
      "\n",
      " [[177 144 116]\n",
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      "  [ 92  67  46]\n",
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      "  [170 140 104]\n",
      "  [216 184 140]\n",
      "  [151 118  84]\n",
      "  [123  92  72]]]\n",
      "y_train[0]:\n",
      " [6]\n",
      "x_train.shape:\n",
      " (50000, 32, 32, 3)\n",
      "y_train.shape:\n",
      " (50000, 1)\n",
      "x_test.shape:\n",
      " (10000, 32, 32, 3)\n",
      "y_test.shape:\n",
      " (10000, 1)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "np.set_printoptions(threshold=np.inf)\n",
    "\n",
    "cifar10 = tf.keras.datasets.cifar10\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
    "\n",
    "# 可视化训练集输入特征的第一个元素\n",
    "plt.imshow(x_train[0])  # 绘制图片\n",
    "plt.show()\n",
    "\n",
    "# 打印出训练集输入特征的第一个元素\n",
    "print(\"x_train[0]:\\n\", x_train[0])\n",
    "# 打印出训练集标签的第一个元素\n",
    "print(\"y_train[0]:\\n\", y_train[0])\n",
    "\n",
    "# 打印出整个训练集输入特征形状\n",
    "print(\"x_train.shape:\\n\", x_train.shape)\n",
    "# 打印出整个训练集标签的形状\n",
    "print(\"y_train.shape:\\n\", y_train.shape)\n",
    "# 打印出整个测试集输入特征的形状\n",
    "print(\"x_test.shape:\\n\", x_test.shape)\n",
    "# 打印出整个测试集标签的形状\n",
    "print(\"y_test.shape:\\n\", y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
